exportPulseDashboardHtmlRender a Duvo Pulse dashboard to a self-contained static HTML snapshot (text/html, data baked in). 404 until the first generation turn has produced HTML; 409 for an openui dashboard, whose substrate this exporter cannot render.
| Name | Required | Description | Default |
|---|---|---|---|
| theme | No | light | |
| artifactId | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and idempotentHint as true, and destructiveHint as false. The description complements these by revealing specific behavioral traits: the need to wait for generation turn (404 state) and the limitation with openui dashboards (409). This adds context beyond annotations. However, it does not mention any auth requirements or data handling specifics.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loads the purpose and output format, then concisely lists edge cases. Every sentence adds unique value with zero waste. It is optimally sized for its content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the parameter count is 2 (1 required), no output schema, and moderate complexity (rendering a dashboard to HTML), the description covers the key behavioral aspects (error states, readiness condition) but does not explain what happens after a successful render or the format of the returned HTML. It is mostly complete but leaves minor gaps about success behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0% (no descriptions in schema parameters), so the description carries full burden here. While the description does not explicitly describe parameters, it implies the role of `artifactId` as identifying the dashboard. It also hints at significance of theme by mentioning 'data baked in' which suggests the output is styled according to the theme parameter. For 0% coverage, this is above baseline but could be more explicit.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'Render' and clearly identifies the resource ('Duvo Pulse dashboard'), stating output format ('self-contained static HTML snapshot'). It distinguishes from siblings like 'getPulseDashboardHtml' and 'getPulseDashboardPdf' by specifying the export nature. However, it doesn't fully clarify how it differs from 'getPulseDashboardVersionHtml', a sibling that also mentions HTML export.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states error conditions ('404 until first generation turn produces HTML', '409 for openui dashboard'), which provides clear context for when not to use this tool. This is helpful guidance. However, it does not name alternative tools for those error cases or broader usage scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Despite detailed descriptions, many tool names are highly ambiguous, with multiple tools covering the same conceptual actions (e.g., acceptClarityCaptureSuggestion vs. acceptClarityTeamAssignmentSuggestion, or the many deleteClarity*Interview tools). The set is so large that distinguishing between, say, listClarityFolders, listClarityProcesses, and listClarityProcessSummaries requires reading deep into descriptions, reducing agent selection accuracy.
The naming convention is predominantly verb_noun (e.g., createClarityProcess, listAgents, deleteQueue), and is remarkably consistent across the 316 tools. There are only minor deviations, such as 'fileSuggestedClarityProcesses' (verb + adjective noun) and 'bulkUpdateCasePriority' (where 'bulk' could be seen as a prefix), but overall the pattern holds strongly.
With 316 tools, this server is extremely oversized for any single agent to manage effectively. The massive number of tools suggests poor modularization—many of these tools likely belong in separate, smaller servers focused on specific domains (e.g., Clarity, Pulse, Agent management). The cognitive load for an agent to choose from 316 options is very high, leading to frequent misselection.
The tool surface covers an extraordinarily wide range of operations across the Duvo platform: agents, runs, cases, queues, Clarity processes, skills, integrations, notifications, teams, and more. Most resource types have full CRUD and lifecycle management. Notable minor gaps exist (e.g., no tools for managing specific notification batch severities dynamically, and some interview management is missing batch operations), but for the platform's scope, coverage is impressively thorough.